Casal2 Test Case MCMC comparisons for BCO
1 BCO comparison of CASAL and Casal2 model configurations
This document compares the results of at least 2 CASAL model configurations (base and at least one sensitivity) and up to 6 Casal2 model configurations (3 BetaDiff and 3 ADOL-C).
The CASAL model sensitivity 1 has a smaller minimisation tolerance value than the CASAL base model (1e-7 vs. 2e-5).
The Casal2 ADOL-C and BetaDiff low tolerance models have a smaller tolerance value than the CASAL base model (1e-7 vs. 2e-5).
1.1 BCO model characteristics
The main characteristics of the Test Case BCO (ling) CASAL model are:
- one stock, ages 3 - 20
- one area
- years 1900 - 2019, projection year 2020
- one time step
- two stock categories: male and female
- natural mortality-at-age is 0.14 for males and 0.20 for females
- sex-specific logistic maturity
- sex-specific von Bertalanffy age-length relationship
- one length-weight relationship (\(W = aL^b\))
- Beverton-Holt stock-recruitment relationship, with steepness (h) 0.75 and \(\sigma_R\) 0.6
- no ageing error
- one survey with sex-specific age-based logistic selectivity
- three fisheries, all with sex-specific length-based logistic selectivity and retained/discarded catches
Observation data include:
- survey index
- fishery CPUE index
- fishery proportions-at-length
Parameters estimated include:
- B0
- male \(L_{\infty}\)
- male age-length CV2
- sex-specific survey logistic selectivity (a50 and ato95)
- YCS (recruitment deviations)
1.2 BCO model MCMC specifications
The CASAL MCMC options include
- start 0
- keep 2500
- length 4000000
- stepsize 0.02
- adaptive_stepsize True
- adapt_at 10000 50000 150000 250000 450000 750000 1400000
- df 2
- burn_in 1000
- proposal_t True
The Casal2 ADOL-C and BetaDiff MCMC options include
- start 0
- keep 2500
- length 4000000
- step_size 0.02
- adapt_stepsize_at 10000 50000 150000 250000 450000 750000 1400000
- proposal_distribution t
- adapt_stepsize_method double_half
1.3 R environment
## [1] "Tue Dec 7 15:29:30 2021"
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux bookworm/sid
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.18.so
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## locale:
## [1] LC_CTYPE=en_NZ.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_NZ.UTF-8 LC_COLLATE=en_NZ.UTF-8
## [5] LC_MONETARY=en_NZ.UTF-8 LC_MESSAGES=en_NZ.UTF-8
## [7] LC_PAPER=en_NZ.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_NZ.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] Casal2_21.11 casal_2.30 devtools_2.4.3 usethis_2.1.3 rlist_0.4.6.2
## [6] ggthemes_4.2.4 gridExtra_2.3 coda_0.19-4 ggmcmc_1.5.1.1 ggplot2_3.3.5
## [11] tidyr_1.1.4 huxtable_5.4.0 dplyr_1.0.7 plyr_1.8.6
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.7 lattice_0.20-45 prettyunits_1.1.1 ps_1.6.0
## [5] assertthat_0.2.1 rprojroot_2.0.2 digest_0.6.29 utf8_1.2.2
## [9] R6_2.5.1 evaluate_0.14 pillar_1.6.4 rlang_0.4.12
## [13] data.table_1.14.2 callr_3.7.0 jquerylib_0.1.4 rmarkdown_2.11
## [17] desc_1.4.0 stringr_1.4.0 munsell_0.5.0 compiler_4.1.2
## [21] xfun_0.28 pkgconfig_2.0.3 pkgbuild_1.2.1 htmltools_0.5.2
## [25] tidyselect_1.1.1 tibble_3.1.6 reshape_0.8.8 fansi_0.5.0
## [29] crayon_1.4.2 withr_2.4.3 grid_4.1.2 jsonlite_1.7.2
## [33] GGally_2.1.2 gtable_0.3.0 lifecycle_1.0.1 DBI_1.1.1
## [37] magrittr_2.0.1 scales_1.1.1 cli_3.1.0 stringi_1.7.6
## [41] cachem_1.0.6 fs_1.5.1 remotes_2.4.2 testthat_3.1.1
## [45] bslib_0.3.1 ellipsis_0.3.2 generics_0.1.1 vctrs_0.3.8
## [49] RColorBrewer_1.1-2 tools_4.1.2 glue_1.5.1 purrr_0.3.4
## [53] processx_3.5.2 pkgload_1.2.4 fastmap_1.1.0 yaml_2.2.1
## [57] colorspace_2.0-2 sessioninfo_1.2.2 memoise_2.0.1 knitr_1.36
## [61] sass_0.4.01.4 CASAL and Casal2 model output
# source('../../R-functions/report_read_in_CASAL_MPD_files.R')
source('../../R-functions/report_read_in_CASAL_MCMC_files.R')source('../../R-functions/report_read_in_Casal2_MPD_files.R')
source('../../R-functions/report_read_in_Casal2_MCMC_files.R')1.5 MCMC diagnostics
1.5.1 Diagnostics for the CASAL and Casal2 model MCMC results
For the diagnostics below, the last 1000 samples for each chain are input into the diagnostic functions.
